# Load all libraries at oncelibrary(tidyverse)library(DT)library(stringr)library(plotly)library(lubridate)library(knitr)# Create directories and load dataif(!dir.exists("data/mp01")) dir.create("data/mp01", recursive=TRUE)GLOBAL_TOP_10_FILE <-"data/mp01/global_top10_alltime.csv"COUNTRY_TOP_10_FILE <-"data/mp01/country_top10_alltime.csv"if(!file.exists(GLOBAL_TOP_10_FILE)){download.file("https://www.netflix.com/tudum/top10/data/all-weeks-global.tsv",destfile = GLOBAL_TOP_10_FILE)}if(!file.exists(COUNTRY_TOP_10_FILE)){download.file("https://www.netflix.com/tudum/top10/data/all-weeks-countries.tsv",destfile = COUNTRY_TOP_10_FILE)}GLOBAL_TOP_10 <-read_tsv(GLOBAL_TOP_10_FILE) %>%mutate(season_title =if_else(season_title =="N/A", NA_character_, season_title))COUNTRY_TOP_10 <-read_tsv(COUNTRY_TOP_10_FILE, na ="N/A") %>%mutate(season_title =if_else(season_title =="N/A", NA_character_, season_title))
Introduction
Netflix is part of daily life for millions of people around the world, with everything from hit TV shows to blockbuster movies. Some titles become popular everywhere, while others are loved more in certain countries. In this project, I look at Netflix’s global and country-specific Top 10 data to find patterns in what people watch, see which shows and movies perform best, and understand how popularity changes across regions.
I study both English and non-English content, showing global hits like Stranger Things as well as local favorites that are gaining attention internationally, especially from countries like Korea and India. The goal is to give the Netflix Public Relations team clear, data-driven insights so they can tell interesting stories about their most successful content.
Answer: Netflix operates in 94 different countries worldwide.
2: Which non-English film has spent the most weeks in the global top 10?
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# Find top non-English films by weeks in Top 10top_non_english_films <- GLOBAL_TOP_10 |>filter(category =="Films (Non-English)") |>group_by(show_title) |>summarise(Total_Weeks =max(cumulative_weeks_in_top_10) ) |>arrange(desc(Total_Weeks)) |>slice_head(n =10) |>rename(`Film Title`= show_title,`Weeks in Top 10`= Total_Weeks )# Get the top film infofilm_name <- top_non_english_films$`Film Title`[1]weeks_count <- top_non_english_films$`Weeks in Top 10`[1]# Display tabletop_non_english_films |>datatable(caption = htmltools::tags$caption(style ='caption-side: top; text-align: center; font-size: 1.1em; font-weight: bold; margin-bottom: 10px;','Top 10 Non-English Films by Weeks in Global Top 10'),options =list(pageLength =10, searching =FALSE) )
Answer: The non-English-language film that has spent the most cumulative weeks in the global Top 10 is “All Quiet on the Western Front”, with 23 weeks.
3: What is the longest film to have appeared in the Netflix global Top 10?
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# Create the table - filter for FILMS onlylongest_films <- GLOBAL_TOP_10 |>filter(!is.na(runtime)) |>filter(category =="Films (English)"| category =="Films (Non-English)") |>group_by(show_title, category) |>summarise(Runtime_Minutes =round(mean(runtime *60)),Best_Position =min(weekly_rank),Weeks_in_Top_10 =max(cumulative_weeks_in_top_10),.groups ="drop" ) |>arrange(desc(Runtime_Minutes)) |>slice_head(n =10) |>rename(`Film Title`= show_title,Category = category,`Runtime (minutes)`= Runtime_Minutes,`Best Top 10 Position`= Best_Position,`Weeks in Top 10`= Weeks_in_Top_10 )# Get answer from the tablefilm_name <- longest_films$`Film Title`[1]runtime_minutes <- longest_films$`Runtime (minutes)`[1]# Display tablelongest_films |>datatable(caption = htmltools::tags$caption(style ='caption-side: top; text-align: center; font-size: 1.1em; font-weight: bold; margin-bottom: 10px;','Top 10 Longest Films in Netflix Global Top 10' ),options =list(pageLength =10, searching =FALSE) )
Answer: Pushpa 2: The Rule (Reloaded Version): It’s the longest non-English film in the Netflix Global Top 10 at 224 minutes, showing that audiences are willing to watch very long films if they’re engaging.
4: Which program has the most total hours of viewership in each category?
Show code
library(dplyr)library(knitr) # for kable()# Get top program by total hours for each categorytop_viewership <- GLOBAL_TOP_10 %>%group_by(category, show_title, season_title) %>%summarise(total_hours =sum(weekly_hours_viewed, na.rm =TRUE), .groups ="drop") %>%group_by(category) %>%slice_max(total_hours, n =1) %>%ungroup() %>%arrange(category)# Print as a clean tablekable(top_viewership, col.names =c("Category", "Show Title", "Season Title", "Total Hours"),digits =0,align ="l")
Category
Show Title
Season Title
Total Hours
Films (English)
KPop Demon Hunters
NA
559100000
Films (Non-English)
Society of the Snow
NA
235900000
TV (English)
Wednesday
Wednesday: Season 1
2043650000
TV (Non-English)
Squid Game
Squid Game: Season 1
2729400000
Answer: The programs with the highest total hours of viewership in each category are:
English Language Film: KPop Demon Hunters
Non-English Language Film: Society of the Snow
English Langauge TV Show: Stranger Things
Non-English Language TV Show: Squid Game
5: Which TV show had the longest run in a country’s Top 10? How long was this run and in what country did it occur?
Show code
# Get top TV show from global datalongest_tv_run <- GLOBAL_TOP_10 |>filter(category %in%c("TV (English)", "TV (Non-English)")) |>group_by(show_title, category) |>summarise(Max_Weeks =max(cumulative_weeks_in_top_10, na.rm =TRUE),Total_Hours_Viewed =sum(weekly_hours_viewed, na.rm =TRUE),.groups ="drop" ) |>arrange(desc(Max_Weeks)) |>slice_head(n =1)show_name <- longest_tv_run$show_titleweeks <- longest_tv_run$Max_Weekstotal_hours <- longest_tv_run$Total_Hours_Viewed
Answer: Squid Game stayed in the global Top 10 for 32 weeks and was watched for 5,048,300,000 hours worldwide.
6: Netflix provides over 200 weeks of service history for all but one country in our data set. Which country is this and when did Netflix cease operations in that country?
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# Count weeks of data per countrycountry_weeks <- COUNTRY_TOP_10 |>group_by(country_name) |>summarise(Weeks_of_Data =n_distinct(week),First_Week =min(week),Last_Week =max(week),.groups ="drop" ) |>arrange(Weeks_of_Data)# Find the country with fewer than 200 weekslimited_country <- country_weeks |>filter(Weeks_of_Data <200) |>slice_head(n =1)country_name <- limited_country$country_nameweeks_available <- limited_country$Weeks_of_Datalast_week <- limited_country$Last_Week
7: What is the total viewership of Squid Game?
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# Calculate total viewership for Squid Game (excluding The Challenge)squid_game_total <- GLOBAL_TOP_10 |>filter(str_detect(show_title, "Squid Game")) |>filter(!str_detect(show_title, "Challenge")) |>summarise(Total_Hours_Viewed =sum(weekly_hours_viewed, na.rm =TRUE),Total_Weeks =n_distinct(week),Seasons =n_distinct(season_title) )total_hours <- squid_game_total$Total_Hours_Viewedtotal_weeks <- squid_game_total$Total_Weeksnum_seasons <- squid_game_total$Seasons
Show code
library(knitr)library(dplyr)# Prepare Squid Game summary tablesquid_game_table <-data.frame(Show_Title ="Squid Game",Total_Hours_Viewed = total_hours,Total_Weeks_in_Top_10 = total_weeks,Number_of_Seasons = num_seasons)# Display as a clean tablekable(squid_game_table,col.names =c("Show Title", "Total Hours Viewed", "Total Weeks in Top 10", "Number of Seasons"),digits =0,align ="l")
Show Title
Total Hours Viewed
Total Weeks in Top 10
Number of Seasons
Squid Game
5048300000
42
3
Answer: The scripted series Squid Game has accumulated 5,048,300,000 total hours viewed
8: How many views did Red Notice (1hr 58min runtime) receive in 2021?
Answer: The TV show that debuted in the Top 10 in the most countries during its first week is Emily in Paris: Season 2.
Press Release 1: Back to Hawkins: The Final Stranger Things Season is Coming!
Stranger Things 5
Hey Stranger Things fans! Season 5 is coming soon, and everyone is excited to go back to Hawkins, Indiana. The show has been super popular since 2016. So far, it’s been in the global Top 10 for 42 weeks and people around the world have watched over 2,043,650,000 hours of it. That’s way more than most other English-language TV shows on Netflix!
Each season keeps getting bigger. Season 4 alone got over 1.2 billion hours viewed, showing how much people love it. And it’s not just popular in the U.S.—Stranger Things hit the Top 10 in tons of countries, like in Serbia it stayed in the Top 10 for 58 weeks!
The Duffer Brothers made the show, and it’s all about a group of teens who find crazy supernatural stuff in their town. There’s also comics, books, games, and even live experiences for fans. Stranger Things is more than a show—it’s a worldwide phenomenon, and Season 5 looks like it’s going to be epic.
Show code
# Country analysis - top 10 onlystranger_things_countries <- COUNTRY_TOP_10 |>filter(str_detect(show_title, "Stranger Things")) |>group_by(country_name) |>summarise(Total_Weeks =n_distinct(week),Max_Cumulative_Weeks =max(cumulative_weeks_in_top_10, na.rm =TRUE),.groups ="drop" ) |>arrange(desc(Total_Weeks)) |>slice_head(n =10) |>rename(Country = country_name,`Total Weeks in Top 10`= Total_Weeks,`Cumulative Weeks in Top 10`= Max_Cumulative_Weeks )stranger_things_countries |>datatable(caption = htmltools::tags$caption(style ='caption-side: top; text-align: center; font-size: 1.1em; font-weight: bold; margin-bottom: 10px;','Top 10 Countries: Stranger Things Engagement' ),options =list(pageLength =10, searching =FALSE, paging =FALSE) )
Netflix has seen incredible growth in India, with Hindi-language films and TV shows reaching millions of viewers every week. Popular programs like RRR (Hindi) and Squid Game (dubbed) have drawn huge audiences, with RRR spending 18 weeks in the global Top 10 and Squid Game: Season 1 accumulating over 2.7 billion hours viewed worldwide. In addition, Through My Window reached 18 weeks in the global Top 10, showing strong local and international engagement.
The data suggests that Netflix’s Hindi-language content is not only a hit in India but also resonates globally, highlighting a growing base of subscribers in one of the world’s largest markets. With over 200 weeks of continuous service in India, Netflix is poised to expand its influence and continue to attract viewers eager for local stories with global appeal.
Show code
# Get top titles in Indiaindia_top <- COUNTRY_TOP_10 |>filter(country_name =="India") |>group_by(show_title, category) |>summarise(India_Weeks =n(),.groups ="drop" ) |>arrange(desc(India_Weeks)) |>slice_head(n =10)# Get weeks in top 10 for other countriesus_weeks <- COUNTRY_TOP_10 |>filter(country_name =="United States") |>group_by(show_title) |>summarise(US_Weeks =n(), .groups ="drop")uk_weeks <- COUNTRY_TOP_10 |>filter(country_name =="United Kingdom") |>group_by(show_title) |>summarise(UK_Weeks =n(), .groups ="drop")japan_weeks <- COUNTRY_TOP_10 |>filter(country_name =="Japan") |>group_by(show_title) |>summarise(Japan_Weeks =n(), .groups ="drop")# Combine all weeksindia_international <- india_top |>left_join(us_weeks, by ="show_title") |>left_join(uk_weeks, by ="show_title") |>left_join(japan_weeks, by ="show_title") |>mutate(Rank =row_number(),US_Weeks =ifelse(is.na(US_Weeks), 0, US_Weeks),UK_Weeks =ifelse(is.na(UK_Weeks), 0, UK_Weeks),Japan_Weeks =ifelse(is.na(Japan_Weeks), 0, Japan_Weeks) ) |>select(Rank, show_title, category, India_Weeks, US_Weeks, UK_Weeks, Japan_Weeks) |>rename(Title = show_title,Category = category,`India Weeks`= India_Weeks,`US Weeks`= US_Weeks,`UK Weeks`= UK_Weeks,`Japan Weeks`= Japan_Weeks )india_international |>datatable(caption = htmltools::tags$caption(style ='caption-side: top; text-align: center; font-size: 1.1em; font-weight: bold; margin-bottom: 10px;','Top 10 India Titles: Weeks in Top 10 by Country' ),options =list(pageLength =10, searching =FALSE, paging =FALSE, scrollX =TRUE) )
Press Release 3: Netflix Brings the World to Your Screen
Compressed Image
Netflix is proud to highlight the success of non-English-language films, showing that audiences love stories from all over the world. Films like All Quiet on the Western Front have spent 23 weeks in the global Top 10, while Pushpa 2: The Rule (Reloaded Version) became the longest film ever to make the list at 224 minutes. Other hits include Blood Red Sky (16 weeks) and K.O. (11 weeks).
These titles show that viewers are willing to watch long and diverse stories, creating opportunities for Netflix to invest in global content. Non-English films are no longer niche; they are now key drivers of international growth, capturing viewers in multiple regions and proving that a great story speaks every language.
